[1]} define interpretability in ML as, “the ability to explain or to present in understandable terms to a human.” Professor Sameer Singh of the University of California at Irvine (UCI), co-inventor of the seminal local interpretable model-agnostic explanation (LIME) technique, defines explanation as a, “collection of visual and/or interactive artifacts that provide a user with sufficient description of a model's behavior to accurately perform tasks like evaluation, trusting, predicting, or improving a model.”From: “Proposed Guidelines for the Responsible Use of Explainable Machine Learning” (presentation only). And [2]} posit that a good explanation occurs when modelers or consumers “can no longer keep asking why” in regards to some ML model behavior. These three thoughtful characterizations link explainability to interpretability, give clarity on explanation, and provide an abstract goal for any explainability task.
